A New Semantic Segmentation Technique for Interference Mitigation in Automotive Radar

Ahmed A. Elsharkawy, Abdallah S. Abdallah, Mohamed W. Fakhr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent autonomous vehicles and Advanced Driver Assistance Systems (ADAS) are equipped with several sensing technologies, including cameras, LiDAR, radar, and ultrasonic. Due to its exceptional features, radar is increasingly utilized in a range of ADAS applications. Unfortunately, this increases the likelihood of radar-to-radar interference, which hinders radar functionality. Numerous research studies have investigated interference mitigation using various traditional signal processing or deep learning techniques. This paper presents a new technique utilizing the U-Net deep neural network (DNN) model for interference mitigation via semantic segmentation in such ADAS scenarios. By comparing the performance of the proposed model to previously published deep-learning-based approaches, our new model has demonstrated promising improvements based on standard evaluation criteria.

Original languageEnglish (US)
Title of host publication2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665491228
DOIs
StatePublished - 2023
Event2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Glasgow, United Kingdom
Duration: Mar 26 2023Mar 29 2023

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2023-March
ISSN (Print)1525-3511

Conference

Conference2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
Country/TerritoryUnited Kingdom
CityGlasgow
Period3/26/233/29/23

All Science Journal Classification (ASJC) codes

  • General Engineering

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